scholarly journals Entrainment model for fully-developed wind farms: Effects of atmospheric stability and an ideal limit for wind farm performance

2018 ◽  
Vol 3 (9) ◽  
Author(s):  
Paolo Luzzatto-Fegiz ◽  
Colm-cille P. Caulfield
Keyword(s):  
Wind Farm ◽  
Atmospheric Stability ◽  
Wind Farms ◽  
Farm Performance ◽  
Ideal Limit

scholarly journals Spatiotemporal Correlations in the Power Output of Wind Farms: On the Impact of Atmospheric Stability

Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1486 ◽  
Author(s):  
Nicolas Tobin ◽  
Adam Lavely ◽  
Sven Schmitz ◽  
Leonardo P. Chamorro
Keyword(s):  
Power Output ◽  
Wind Farm ◽  
Statistical Significance ◽  
Atmospheric Stability ◽  
Wind Farms ◽  
Temporal Correlations ◽  
Theoretical Predictions ◽  
Power Variance ◽  
Ambient Turbulence ◽  
The Impact

The dependence of temporal correlations in the power output of wind-turbine pairs on atmospheric stability is explored using theoretical arguments and wind-farm large-eddy simulations. For this purpose, a range of five distinct stability regimes, ranging from weakly stable to moderately convective, were investigated with the same aligned wind-farm layout used among simulations. The coherence spectrum between turbine pairs in each simulation was compared to theoretical predictions. We found with high statistical significance (p < 0.01) that higher levels of atmospheric instability lead to higher coherence between turbines, with wake motions reducing correlations up to 40%. This is attributed to higher dominance of atmospheric motions over wakes in strongly unstable flows. Good agreement resulted with the use of an empirical model for wake-added turbulence to predict the variation of turbine power coherence with ambient turbulence intensity (R 2 = 0.82), though other empirical relations may be applicable. It was shown that improperly accounting for turbine–turbine correlations can substantially impact power variance estimates on the order of a factor of 4.

Probabilistic Models for Wind Turbine and Wind Farm Performance

2011 ◽  
Vol 133 (4) ◽  
Author(s):  
Sanjay R. Arwade ◽  
Matthew A. Lackner ◽  
Mircea D. Grigoriu
Keyword(s):  
Markov Model ◽  
Wind Turbine ◽  
Large Scale ◽  
Wind Farm ◽  
Probabilistic Models ◽  
Performance Metrics ◽  
Wind Farms ◽  
Component Reliability ◽  
Mc Simulation ◽  
Farm Performance

A Markov model for the performance of wind turbines is developed that accounts for component reliability and the effect of wind speed and turbine capacity on component reliability. The model is calibrated to the observed performance of offshore turbines in the north of Europe, and uses wind records obtained from the coast of the state of Maine in the northeast United States in simulation. Simulation results indicate availability of 0.91, with mean residence time in the operating state that is nearly exponential and has a mean of 42 days. Using a power curve typical for a 2.5 MW turbine, the capacity factor is found to be beta distributed and highly non-Gaussian. Noticeable seasonal variation in turbine and farm performance metrics are observed and result from seasonal fluctuations in the characteristics of the wind record. The input parameters to the Markov model, as defined in this paper, are limited to those for which field data are available for calibration. Nevertheless, the framework of the model is readily adaptable to include, for example: site specific conditions; turbine details; wake induced loading effects; component redundancies; and dependencies. An on-off model is introduced as an approximation to the stochastic process describing the operating state of a wind turbine, and from this on-off process an Ornstein–Uhlenbeck (O–U) process is developed as a model for the availability of a wind farm. The O–U model agrees well with Monte Carlo (MC) simulation of the Markov model and is accepted as a valid approximation. Using the O–U model in design and management of large wind farms will be advantageous because it can provide statistics of wind farm performance without resort to intensive large scale MC simulation.

scholarly journals The Role of Atmospheric Stability and Turbulence in Offshore Wind-Farm Wakes in the German Bight

2021 ◽  
Author(s):  
Andreas Platis ◽  
Marie Hundhausen ◽  
Astrid Lampert ◽  
Stefan Emeis ◽  
Jens Bange
Keyword(s):  
German Bight ◽  
Wind Farm ◽  
Atmospheric Stability ◽  
Wind Farms ◽  
Offshore Wind ◽  
Lapse Rate ◽  
Offshore Wind Farm ◽  
Near Surface ◽  
Stable Conditions ◽  
The Stability

AbstractAirborne meteorological in situ measurements as well as stationary measurements at the offshore masts FINO1 and FINO3 in the German Bight are evaluated in order to examine the hypothesis that the wake dissipation downstream of large offshore wind farms depends on atmospheric stability. A long-term study of the mast data for the years 2016 and 2017 demonstrates a clear dependence of stability on the wind direction. Stable conditions are predominantly expected during southerly winds coming from the land. The analysis of various stability and turbulence criteria shows that the lapse rate is the most robust parameter for stability classification in the German Bight, but further implies that stability depends on the measurement height. A near-surface (0 to 30 m), predominantly convective, layer is present and more stable conditions are found aloft (55 to 95 m). Combing the stability data with the airborne measurements of the offshore wind-farm wakes reveals the trend of a correlation between longer wake lengths and an increase in the initial wind-speed deficit downwind of a wind farm with stronger thermal stability. However, the stability correlation criteria with the wake length downstream of the four investigated wind farms, Godewind, Amrumbank West, Meerwind Süd/Ost, and Nordsee Ost, contain large variance. It is assumed that the observed scattering is due to the influence of the wind-farm architecture and temperature inversions around hub height. These, however, are crucial for the classification of stability and illustrate the complexity of a clear stability metric.

scholarly journals Three-Dimensional Structure of Wind Turbine Wakes as Measured by Scanning Lidar

2017 ◽  
Author(s):  
Nicola Bodini ◽  
Dino Zardi ◽  
Julie K. Lundquist
Keyword(s):  
Wind Farm ◽  
Atmospheric Stability ◽  
Three Dimensional ◽  
Leading Edge ◽  
Wind Farms ◽  
Layout Optimization ◽  
Dimensional Structure ◽  
Strong Wind ◽  
Three Dimensional Structure ◽  
Stable Conditions

Abstract. The slower wind speeds and increased turbulence that are characteristic of turbine wakes have considerable consequences on large wind farms: turbines located downwind generate less power and experience increased turbulent loads. The structures of wakes and their downwind impacts are sensitive to wind speed and atmospheric variability. Wake characterization can provide important insights for turbine layout optimization in view of decreasing the cost of wind energy. The CWEX-13 field campaign, which took place between June and September 2013 in a wind farm in Iowa, was designed to explore the interaction of multiple wakes in a range of atmospheric stability conditions. Based on lidar wind measurements, we extend, present, and apply a quantitative algorithm to assess wake parameters such as the velocity deficits, the size of the wake boundaries, and the location of the wake centerlines. We focus on wakes from a row of four turbines at the leading edge of the wind farm to explore variations between wakes from the edge of the row (outer wakes) and those from turbines in the center of the row (inner wakes). Using multiple horizontal scans at different elevations, a three-dimensional structure of wakes from the row of turbines can be created. Wakes erode very quickly during unstable conditions, and can in fact be detected primarily in stable conditions in the conditions measured here. During stable conditions, important differences emerge between the wakes of inner turbines and the wakes of outer turbines. Further, the strong wind veer associated with stable conditions results in a stretching of the wake structures, and this stretching manifests differently for inner and outer wakes. These insights can be incorporated into low-order wake models for wind farm layout optimization or for wind power forecasting.

Two Corrections for Turbulent Kinetic Energy Generated by Wind Farms in the WRF Model

2020 ◽  
Vol 148 (12) ◽  
pp. 4823-4835
Author(s):  
Cristina L. Archer ◽  
Sicheng Wu ◽  
Yulong Ma ◽  
Pedro A. Jiménez
Keyword(s):  
Wind Speed ◽  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Wind Farm ◽  
Atmospheric Stability ◽  
Wrf Model ◽  
Wind Farms ◽  
Near Surface ◽  
Large Eddy ◽  
The Way

AbstractAs wind farms grow in number and size worldwide, it is important that their potential impacts on the environment are studied and understood. The Fitch parameterization implemented in the Weather Research and Forecasting (WRF) Model since version 3.3 is a widely used tool today to study such impacts. We identified two important issues related to the way the added turbulent kinetic energy (TKE) generated by a wind farm is treated in the WRF Model with the Fitch parameterization. The first issue is a simple “bug” in the WRF code, and the second issue is the excessive value of a coefficient, called CTKE, that relates TKE to the turbine electromechanical losses. These two issues directly affect the way that a wind farm wake evolves, and they impact properties like near-surface temperature and wind speed at the wind farm as well as behind it in the wake. We provide a bug fix and a revised value of CTKE that is one-quarter of the original value. This 0.25 correction factor is empirical; future studies should examine its dependence on parameters such as atmospheric stability, grid resolution, and wind farm layout. We present the results obtained with the Fitch parameterization in the WRF Model for a single turbine with and without the bug fix and the corrected CTKE and compare them with high-fidelity large-eddy simulations. These two issues have not been discovered before because they interact with one another in such a way that their combined effect is a somewhat realistic vertical TKE profile at the wind farm and a realistic wind speed deficit in the wake. All WRF simulations that used the Fitch wind farm parameterization are affected, and their conclusions may need to be revisited.

Wind Flow Conditions in Offshore Wind Farms: Validation and Application of a CFD Wake Model

Green ◽  
2013 ◽  
Vol 3 (1) ◽  
Author(s):  
Annette Westerhellweg ◽  
Beatriz Cañadillas ◽  
Friederike Kinder ◽  
Thomas Neumann
Keyword(s):  
Wind Farm ◽  
Atmospheric Stability ◽  
Wind Farms ◽  
Offshore Wind ◽  
Wind Flow ◽  
Flow Conditions ◽  
Offshore Wind Farms ◽  
Speed Reduction ◽  
Wake Model ◽  
The Impact

AbstractSince August 2009, the first German offshore wind farm ‘alpha ventus’ is operating close to the wind measurement platform FINO1. Within the research project RAVE-OWEA the wind flow conditions in ‘alpha ventus’ were assessed in detail, simulated with a CFD wake model and compared with the measurements. Wind data measured at FINO1 have been evaluated for wind speed reduction and turbulence increase in the wake. Additionally operational data were evaluated for the farm efficiency. The atmospheric stability has been evaluated by temperature measurements of air and water and the impact of atmospheric stability on the wind conditions in the wake has been assessed. As an application of CFD models the generation of power matrices is introduced. Power matrices can be used for the continual monitoring of the single wind turbines in the wind farm. A power matrix based on CFD simulations has been created for ‘alpha ventus’ and tested against the measured data.

scholarly journals Three-dimensional structure of wind turbine wakes as measured by scanning lidar

2017 ◽  
Vol 10 (8) ◽  
pp. 2881-2896 ◽  
Author(s):  
Nicola Bodini ◽  
Dino Zardi ◽  
Julie K. Lundquist
Keyword(s):  
Wind Farm ◽  
Atmospheric Stability ◽  
Three Dimensional ◽  
Leading Edge ◽  
Wind Farms ◽  
Layout Optimization ◽  
Dimensional Structure ◽  
Strong Wind ◽  
Three Dimensional Structure ◽  
Stable Conditions

Abstract. The lower wind speeds and increased turbulence that are characteristic of turbine wakes have considerable consequences on large wind farms: turbines located downwind generate less power and experience increased turbulent loads. The structures of wakes and their downwind impacts are sensitive to wind speed and atmospheric variability. Wake characterization can provide important insights for turbine layout optimization in view of decreasing the cost of wind energy. The CWEX-13 field campaign, which took place between June and September 2013 in a wind farm in Iowa, was designed to explore the interaction of multiple wakes in a range of atmospheric stability conditions. Based on lidar wind measurements, we extend, present, and apply a quantitative algorithm to assess wake parameters such as the velocity deficits, the size of the wake boundaries, and the location of the wake centerlines. We focus on wakes from a row of four turbines at the leading edge of the wind farm to explore variations between wakes from the edge of the row (outer wakes) and those from turbines in the center of the row (inner wakes). Using multiple horizontal scans at different elevations, a three-dimensional structure of wakes from the row of turbines can be created. Wakes erode very quickly during unstable conditions and can in fact be detected primarily in stable conditions in the conditions measured here. During stable conditions, important differences emerge between the wakes of inner turbines and the wakes of outer turbines. Further, the strong wind veer associated with stable conditions results in a stretching of the wake structures, and this stretching manifests differently for inner and outer wakes. These insights can be incorporated into low-order wake models for wind farm layout optimization or for wind power forecasting.

scholarly journals Multi-Scale Simulation of Wind Farm Performance during a Frontal Passage

Atmosphere ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 245 ◽  
Author(s):  
Robert S. Arthur ◽  
Jeffrey D. Mirocha ◽  
Nikola Marjanovic ◽  
Brian D. Hirth ◽  
John L. Schroeder ◽  
...  
Keyword(s):  
Turbulent Flows ◽  
Power Output ◽  
Wind Farm ◽  
Wrf Model ◽  
Wind Farms ◽  
Atmospheric Conditions ◽  
Model Framework ◽  
Frontal Passage ◽  
Multi Scale ◽  
Farm Performance

Predicting the response of wind farms to changing flow conditions is necessary for optimal design and operation. In this work, simulation and analysis of a frontal passage through a utility scale wind farm is achieved for the first time using a seamless multi-scale modeling approach. A generalized actuator disk (GAD) wind turbine model is used to represent turbine–flow interaction, and results are compared to novel radar observations during the frontal passage. The Weather Research and Forecasting (WRF) model is employed with a nested grid setup that allows for coupling between multi-scale atmospheric conditions and turbine response. Starting with mesoscale forcing, the atmosphere is dynamically downscaled to the region of interest, where the interaction between turbulent flows and individual wind turbines is simulated with 10 m grid spacing. Several improvements are made to the GAD model to mimic realistic turbine operation, including a yawing capability and a power output calculation. Ultimately, the model is able to capture both the dynamics of the frontal passage and the turbine response; predictions show good agreement with observed background velocity, turbine wake structure, and power output after accounting for a phase shift in the mesoscale forcing. This study demonstrates the utility of the WRF-GAD model framework for simulating wind farm performance under complex atmospheric conditions.

scholarly journals An analysis of offshore wind farm SCADA measurements to identify key parameters influencing the magnitude of wake effects

2017 ◽  
Vol 2 (2) ◽  
pp. 477-490 ◽  
Author(s):  
Niko Mittelmeier ◽  
Julian Allin ◽  
Tomas Blodau ◽  
Davide Trabucchi ◽  
Gerald Steinfeld ◽  
...  
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Average Power ◽  
Atmospheric Stability ◽  
Wind Farms ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Offshore Wind Farms ◽  
Partial Load ◽  
Wake Effects

Abstract. For offshore wind farms, wake effects are among the largest sources of losses in energy production. At the same time, wake modelling is still associated with very high uncertainties. Therefore current research focusses on improving wake model predictions. It is known that atmospheric conditions, especially atmospheric stability, crucially influence the magnitude of those wake effects. The classification of atmospheric stability is usually based on measurements from met masts, buoys or lidar (light detection and ranging). In offshore conditions these measurements are expensive and scarce. However, every wind farm permanently produces SCADA (supervisory control and data acquisition) measurements. The objective of this study is to establish a classification for the magnitude of wake effects based on SCADA data. This delivers a basis to fit engineering wake models better to the ambient conditions in an offshore wind farm. The method is established with data from two offshore wind farms which each have a met mast nearby. A correlation is established between the stability classification from the met mast and signals within the SCADA data from the wind farm. The significance of these new signals on power production is demonstrated with data from two wind farms with met mast and long-range lidar measurements. Additionally, the method is validated with data from another wind farm without a met mast. The proposed signal consists of a good correlation between the standard deviation of active power divided by the average power of wind turbines in free flow with the ambient turbulence intensity (TI) when the wind turbines were operating in partial load. It allows us to distinguish between conditions with different magnitudes of wake effects. The proposed signal is very sensitive to increased turbulence induced by neighbouring turbines and wind farms, even at a distance of more than 38 rotor diameters.

scholarly journals Estimating the wake deflection downstream of a wind turbine in different atmospheric stabilities: an LES study

2016 ◽  
Vol 1 (2) ◽  
pp. 129-141 ◽  
Author(s):  
Lukas Vollmer ◽  
Gerald Steinfeld ◽  
Detlev Heinemann ◽  
Martin Kühn
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Atmospheric Stability ◽  
Wind Farms ◽  
Stability Control ◽  
Energy Yield ◽  
Multiple Sources ◽  
Ambient Wind ◽  
Precise Knowledge ◽  
Temporal Averaging

Abstract. An intentional yaw misalignment of wind turbines is currently discussed as one possibility to increase the overall energy yield of wind farms. The idea behind this control is to decrease wake losses of downstream turbines by altering the wake trajectory of the controlled upwind turbines. For an application of such an operational control, precise knowledge about the inflow wind conditions, the magnitude of wake deflection by a yawed turbine and the propagation of the wake is crucial. The dependency of the wake deflection on the ambient wind conditions as well as the uncertainty of its trajectory are not sufficiently covered in current wind farm control models. In this study we analyze multiple sources that contribute to the uncertainty of the estimation of the wake deflection downstream of yawed wind turbines in different ambient wind conditions. We find that the wake shapes and the magnitude of deflection differ in the three evaluated atmospheric boundary layers of neutral, stable and unstable thermal stability. Uncertainty in the wake deflection estimation increases for smaller temporal averaging intervals. We also consider the choice of the method to define the wake center as a source of uncertainty as it modifies the result. The variance of the wake deflection estimation increases with decreasing atmospheric stability. Control of the wake position in a highly convective environment is therefore not recommended.

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